烂翻译系列之Rx.NET介绍第二版——第二部分——从事件到洞察

We live in an age where data is being created, stored, and distributed at a phenomenal rate. Consuming this data can be overwhelming, like trying to drink directly from a fire hose. We need the ability to identify the important data, meaning we need ways to determine what is and is not relevant. We need to take groups of data and process them collectively to discover patterns or other information that might not be apparent from any individual raw input. Users, customers and managers need to do this with more data than ever before, while still delivering higher performance and more useful outputs.

我们生活在一个数据被以惊人的速度创建、存储和分发的时代。消费这些数据可能会让人不知所措，就像试图直接从消防水龙带中喝水一样。我们需要有能力识别重要的数据，这意味着我们需要确定哪些数据是相关的，哪些是不相关的。我们需要将数据集进行分组并集体处理，以发现可能从任何单个原始输入中都看不出来的模式或其他信息。用户、客户和管理人员需要用比以往更多的数据来做这件事，同时仍然要提供更高的性能和更有用的输出。

Rx provides some powerful mechanisms for extracting meaningful insights from raw data streams. This is one of the main reasons for representing information as IObservable<T> streams in the first place. The preceding chapter showed how to create an observable sequence, so now we will look at how to exploit the power this has unlocked using the the various Rx methods that can process and transform an observable sequence.

Rx 提供了一些强大的机制，用于从原始数据流中提取有意义的见解。这首先就是为什么将信息表示为 IObservable<T> 流的主要原因之一。前一章展示了如何创建一个可观察的序列，所以现在我们将看看如何使用各种 Rx 方法来处理和转换一个可观察的序列，从而利用这种解锁的能力。

Rx supports most of the standard LINQ operators. It also defines numerous additional operators. These fall broadly into categories, and each of the following chapters tackles one category:

Rx 支持大多数标准的 LINQ 运算符。它还定义了许多额外的运算符。这些运算符大致可以分为几类，以下各章将分别介绍这些类别：

• Filtering    筛选
• Transformation    转换
• Aggregation    聚合
• Partitioning    分区
• Combination    组合